Overview

Dataset statistics

Number of variables27
Number of observations24720
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 MiB
Average record size in memory216.0 B

Variable types

Numeric20
Categorical7

Alerts

Operation Setting 3 has constant value "100.0"Constant
Sensor Measure 1 has constant value "518.67"Constant
Sensor Measure 5 has constant value "14.62"Constant
Sensor Measure 16 has constant value "0.03"Constant
Sensor Measure 18 has constant value "2388"Constant
Sensor Measure 19 has constant value "100.0"Constant
Cycle is highly overall correlated with RULHigh correlation
RUL is highly overall correlated with Cycle and 8 other fieldsHigh correlation
Sensor Measure 10 is highly overall correlated with Sensor Measure 12 and 8 other fieldsHigh correlation
Sensor Measure 11 is highly overall correlated with RUL and 6 other fieldsHigh correlation
Sensor Measure 12 is highly overall correlated with Sensor Measure 10 and 5 other fieldsHigh correlation
Sensor Measure 13 is highly overall correlated with RUL and 8 other fieldsHigh correlation
Sensor Measure 14 is highly overall correlated with Sensor Measure 10 and 3 other fieldsHigh correlation
Sensor Measure 15 is highly overall correlated with Sensor Measure 10 and 5 other fieldsHigh correlation
Sensor Measure 17 is highly overall correlated with RUL and 6 other fieldsHigh correlation
Sensor Measure 2 is highly overall correlated with RUL and 6 other fieldsHigh correlation
Sensor Measure 20 is highly overall correlated with Sensor Measure 10 and 4 other fieldsHigh correlation
Sensor Measure 21 is highly overall correlated with Sensor Measure 10 and 4 other fieldsHigh correlation
Sensor Measure 3 is highly overall correlated with RUL and 6 other fieldsHigh correlation
Sensor Measure 4 is highly overall correlated with RUL and 6 other fieldsHigh correlation
Sensor Measure 6 is highly overall correlated with Sensor Measure 15High correlation
Sensor Measure 7 is highly overall correlated with Sensor Measure 10 and 5 other fieldsHigh correlation
Sensor Measure 8 is highly overall correlated with RUL and 8 other fieldsHigh correlation
Sensor Measure 9 is highly overall correlated with RUL and 4 other fieldsHigh correlation
Sensor Measure 10 is highly imbalanced (71.9%)Imbalance
Operation Setting 1 has 413 (1.7%) zerosZeros
Operation Setting 2 has 2451 (9.9%) zerosZeros

Reproduction

Analysis started2024-06-01 18:41:59.464345
Analysis finished2024-06-01 18:42:59.671630
Duration1 minute and 0.21 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

UnitNumber
Real number (ℝ)

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.631877
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:42:59.869840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q123
median47
Q374
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.348985
Coefficient of variation (CV)0.60349275
Kurtosis-1.2409105
Mean48.631877
Median Absolute Deviation (MAD)26
Skewness0.097954005
Sum1202180
Variance861.36292
MonotonicityIncreasing
2024-06-01T20:43:00.055947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 525
 
2.1%
24 494
 
2.0%
96 491
 
2.0%
10 481
 
1.9%
34 459
 
1.9%
18 447
 
1.8%
7 424
 
1.7%
71 409
 
1.7%
9 406
 
1.6%
94 392
 
1.6%
Other values (90) 20192
81.7%
ValueCountFrequency (%)
1 259
1.0%
2 253
1.0%
3 222
0.9%
4 272
1.1%
5 213
0.9%
6 278
1.1%
7 424
1.7%
8 267
1.1%
9 406
1.6%
10 481
1.9%
ValueCountFrequency (%)
100 152
 
0.6%
99 145
 
0.6%
98 307
1.2%
97 275
1.1%
96 491
2.0%
95 166
 
0.7%
94 392
1.6%
93 171
 
0.7%
92 158
 
0.6%
91 156
 
0.6%

Cycle
Real number (ℝ)

HIGH CORRELATION 

Distinct525
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.07706
Minimum1
Maximum525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:00.217687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q162
median124
Q3191
95-th percentile333.05
Maximum525
Range524
Interquartile range (IQR)129

Descriptive statistics

Standard deviation98.846675
Coefficient of variation (CV)0.71073312
Kurtosis0.82037479
Mean139.07706
Median Absolute Deviation (MAD)64
Skewness0.96779702
Sum3437985
Variance9770.6652
MonotonicityNot monotonic
2024-06-01T20:43:00.376733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
0.4%
74 100
 
0.4%
94 100
 
0.4%
95 100
 
0.4%
96 100
 
0.4%
97 100
 
0.4%
98 100
 
0.4%
99 100
 
0.4%
100 100
 
0.4%
101 100
 
0.4%
Other values (515) 23720
96.0%
ValueCountFrequency (%)
1 100
0.4%
2 100
0.4%
3 100
0.4%
4 100
0.4%
5 100
0.4%
6 100
0.4%
7 100
0.4%
8 100
0.4%
9 100
0.4%
10 100
0.4%
ValueCountFrequency (%)
525 1
< 0.1%
524 1
< 0.1%
523 1
< 0.1%
522 1
< 0.1%
521 1
< 0.1%
520 1
< 0.1%
519 1
< 0.1%
518 1
< 0.1%
517 1
< 0.1%
516 1
< 0.1%

Operation Setting 1
Real number (ℝ)

ZEROS 

Distinct160
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.3741909 × 10-5
Minimum-0.0086
Maximum0.0086
Zeros413
Zeros (%)1.7%
Negative12211
Negative (%)49.4%
Memory size193.2 KiB
2024-06-01T20:43:00.586215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0086
5-th percentile-0.0037
Q1-0.0015
median-0
Q30.0015
95-th percentile0.0036
Maximum0.0086
Range0.0172
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0021935446
Coefficient of variation (CV)-92.391246
Kurtosis-0.0060308774
Mean-2.3741909 × 10-5
Median Absolute Deviation (MAD)0.0015
Skewness-0.012890968
Sum-0.5869
Variance4.8116378 × 10-6
MonotonicityNot monotonic
2024-06-01T20:43:00.763075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0002 469
 
1.9%
0.0002 460
 
1.9%
0.0001 458
 
1.9%
0.0007 456
 
1.8%
-0.0009 456
 
1.8%
-0.0001 453
 
1.8%
0.0005 453
 
1.8%
0.0004 450
 
1.8%
0.0003 449
 
1.8%
-0.0003 448
 
1.8%
Other values (150) 20168
81.6%
ValueCountFrequency (%)
-0.0086 1
 
< 0.1%
-0.0085 1
 
< 0.1%
-0.0082 2
< 0.1%
-0.0079 1
 
< 0.1%
-0.0078 2
< 0.1%
-0.0075 2
< 0.1%
-0.0074 1
 
< 0.1%
-0.0073 3
< 0.1%
-0.0072 3
< 0.1%
-0.0071 2
< 0.1%
ValueCountFrequency (%)
0.0086 1
 
< 0.1%
0.008 2
< 0.1%
0.0078 1
 
< 0.1%
0.0077 1
 
< 0.1%
0.0076 1
 
< 0.1%
0.0074 2
< 0.1%
0.0073 1
 
< 0.1%
0.0072 4
< 0.1%
0.0071 2
< 0.1%
0.007 2
< 0.1%

Operation Setting 2
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0687702 × 10-6
Minimum-0.0006
Maximum0.0007
Zeros2451
Zeros (%)9.9%
Negative11024
Negative (%)44.6%
Memory size193.2 KiB
2024-06-01T20:43:00.912682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0004
Q1-0.0002
median-0
Q30.0003
95-th percentile0.0005
Maximum0.0007
Range0.0013
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00029404289
Coefficient of variation (CV)58.010696
Kurtosis-1.1377038
Mean5.0687702 × 10-6
Median Absolute Deviation (MAD)0.0003
Skewness0.0096697015
Sum0.1253
Variance8.6461219 × 10-8
MonotonicityNot monotonic
2024-06-01T20:43:01.058107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
-0.0002 2531
10.2%
-0.0003 2506
10.1%
0.0004 2471
10.0%
0.0003 2465
10.0%
0.0002 2456
9.9%
0 2451
9.9%
0.0001 2442
9.9%
-0.0004 2414
9.8%
-0.0001 2398
9.7%
0.0005 1319
5.3%
Other values (4) 1267
5.1%
ValueCountFrequency (%)
-0.0006 28
 
0.1%
-0.0005 1147
4.6%
-0.0004 2414
9.8%
-0.0003 2506
10.1%
-0.0002 2531
10.2%
-0.0001 2398
9.7%
0 2451
9.9%
0.0001 2442
9.9%
0.0002 2456
9.9%
0.0003 2465
10.0%
ValueCountFrequency (%)
0.0007 10
 
< 0.1%
0.0006 82
 
0.3%
0.0005 1319
5.3%
0.0004 2471
10.0%
0.0003 2465
10.0%
0.0002 2456
9.9%
0.0001 2442
9.9%
0 2451
9.9%
-0.0001 2398
9.7%
-0.0002 2531
10.2%

Operation Setting 3
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
100.0
24720 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters123600
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 24720
100.0%

Length

2024-06-01T20:43:01.267488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:43:01.380496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 24720
100.0%

Most occurring characters

ValueCountFrequency (%)
0 74160
60.0%
1 24720
 
20.0%
. 24720
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74160
60.0%
1 24720
 
20.0%
. 24720
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74160
60.0%
1 24720
 
20.0%
. 24720
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74160
60.0%
1 24720
 
20.0%
. 24720
 
20.0%

Sensor Measure 1
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
518.67
24720 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters148320
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row518.67
2nd row518.67
3rd row518.67
4th row518.67
5th row518.67

Common Values

ValueCountFrequency (%)
518.67 24720
100.0%

Length

2024-06-01T20:43:01.548780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:43:01.682194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
518.67 24720
100.0%

Most occurring characters

ValueCountFrequency (%)
5 24720
16.7%
1 24720
16.7%
8 24720
16.7%
. 24720
16.7%
6 24720
16.7%
7 24720
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 24720
16.7%
1 24720
16.7%
8 24720
16.7%
. 24720
16.7%
6 24720
16.7%
7 24720
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 24720
16.7%
1 24720
16.7%
8 24720
16.7%
. 24720
16.7%
6 24720
16.7%
7 24720
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 24720
16.7%
1 24720
16.7%
8 24720
16.7%
. 24720
16.7%
6 24720
16.7%
7 24720
16.7%

Sensor Measure 2
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.45786
Minimum640.84
Maximum645.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:01.846741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum640.84
5-th percentile641.69
Q1642.08
median642.4
Q3642.79
95-th percentile643.41
Maximum645.11
Range4.27
Interquartile range (IQR)0.71

Descriptive statistics

Standard deviation0.52303114
Coefficient of variation (CV)0.00081410964
Kurtosis0.026564495
Mean642.45786
Median Absolute Deviation (MAD)0.35
Skewness0.44637839
Sum15881558
Variance0.27356157
MonotonicityNot monotonic
2024-06-01T20:43:02.075259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.27 226
 
0.9%
642.24 220
 
0.9%
642.37 215
 
0.9%
642.15 213
 
0.9%
642.19 212
 
0.9%
642.2 210
 
0.8%
642.39 205
 
0.8%
642.22 204
 
0.8%
642.36 202
 
0.8%
642.09 199
 
0.8%
Other values (324) 22614
91.5%
ValueCountFrequency (%)
640.84 1
 
< 0.1%
640.86 1
 
< 0.1%
640.9 1
 
< 0.1%
640.99 2
< 0.1%
641.03 1
 
< 0.1%
641.05 2
< 0.1%
641.06 1
 
< 0.1%
641.11 2
< 0.1%
641.12 3
< 0.1%
641.13 2
< 0.1%
ValueCountFrequency (%)
645.11 1
 
< 0.1%
644.71 1
 
< 0.1%
644.47 1
 
< 0.1%
644.45 3
< 0.1%
644.41 2
< 0.1%
644.4 2
< 0.1%
644.39 1
 
< 0.1%
644.37 1
 
< 0.1%
644.36 1
 
< 0.1%
644.34 1
 
< 0.1%

Sensor Measure 3
Real number (ℝ)

HIGH CORRELATION 

Distinct3358
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1588.0792
Minimum1564.3
Maximum1615.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:02.291749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1564.3
5-th percentile1577.8195
Q11583.28
median1587.52
Q31592.4125
95-th percentile1600.2405
Maximum1615.39
Range51.09
Interquartile range (IQR)9.1325

Descriptive statistics

Standard deviation6.8104181
Coefficient of variation (CV)0.0042884625
Kurtosis-0.044067335
Mean1588.0792
Median Absolute Deviation (MAD)4.54
Skewness0.34762841
Sum39257317
Variance46.381794
MonotonicityNot monotonic
2024-06-01T20:43:02.541377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1585.56 27
 
0.1%
1586.92 26
 
0.1%
1587.02 25
 
0.1%
1586.42 24
 
0.1%
1585.93 24
 
0.1%
1583.99 23
 
0.1%
1586.28 23
 
0.1%
1590.38 23
 
0.1%
1590.22 23
 
0.1%
1589.57 23
 
0.1%
Other values (3348) 24479
99.0%
ValueCountFrequency (%)
1564.3 1
< 0.1%
1566.34 1
< 0.1%
1566.77 1
< 0.1%
1566.86 1
< 0.1%
1567.22 1
< 0.1%
1567.75 1
< 0.1%
1568.14 1
< 0.1%
1568.77 1
< 0.1%
1568.93 1
< 0.1%
1569.07 1
< 0.1%
ValueCountFrequency (%)
1615.39 2
< 0.1%
1615.01 1
< 0.1%
1613.83 1
< 0.1%
1612.73 1
< 0.1%
1612.52 1
< 0.1%
1612.36 1
< 0.1%
1612.3 1
< 0.1%
1612.28 1
< 0.1%
1612.24 1
< 0.1%
1611.91 1
< 0.1%

Sensor Measure 4
Real number (ℝ)

HIGH CORRELATION 

Distinct4383
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1404.4712
Minimum1377.06
Maximum1441.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:02.724274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1377.06
5-th percentile1390.9695
Q11397.1875
median1402.91
Q31410.6
95-th percentile1423.28
Maximum1441.16
Range64.1
Interquartile range (IQR)13.4125

Descriptive statistics

Standard deviation9.7731783
Coefficient of variation (CV)0.0069586178
Kurtosis-0.11388759
Mean1404.4712
Median Absolute Deviation (MAD)6.49
Skewness0.59652256
Sum34718528
Variance95.515015
MonotonicityNot monotonic
2024-06-01T20:43:02.897403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1398.12 26
 
0.1%
1398 21
 
0.1%
1403.33 21
 
0.1%
1400.6 20
 
0.1%
1398.26 20
 
0.1%
1399.55 20
 
0.1%
1396.5 20
 
0.1%
1396.33 20
 
0.1%
1397.9 19
 
0.1%
1395.28 19
 
0.1%
Other values (4373) 24514
99.2%
ValueCountFrequency (%)
1377.06 1
< 0.1%
1378.37 1
< 0.1%
1380.3 1
< 0.1%
1380.31 1
< 0.1%
1380.43 1
< 0.1%
1380.46 1
< 0.1%
1380.56 1
< 0.1%
1380.85 1
< 0.1%
1381.45 1
< 0.1%
1381.6 1
< 0.1%
ValueCountFrequency (%)
1441.16 1
< 0.1%
1439.98 1
< 0.1%
1439.48 1
< 0.1%
1439.12 1
< 0.1%
1439.09 1
< 0.1%
1438.32 1
< 0.1%
1437.97 1
< 0.1%
1437.76 1
< 0.1%
1437.5 1
< 0.1%
1436.4 1
< 0.1%

Sensor Measure 5
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
14.62
24720 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters123600
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14.62
2nd row14.62
3rd row14.62
4th row14.62
5th row14.62

Common Values

ValueCountFrequency (%)
14.62 24720
100.0%

Length

2024-06-01T20:43:03.124052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:43:03.220530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
14.62 24720
100.0%

Most occurring characters

ValueCountFrequency (%)
1 24720
20.0%
4 24720
20.0%
. 24720
20.0%
6 24720
20.0%
2 24720
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 24720
20.0%
4 24720
20.0%
. 24720
20.0%
6 24720
20.0%
2 24720
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 24720
20.0%
4 24720
20.0%
. 24720
20.0%
6 24720
20.0%
2 24720
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 24720
20.0%
4 24720
20.0%
. 24720
20.0%
6 24720
20.0%
2 24720
20.0%

Sensor Measure 6
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.595841
Minimum21.45
Maximum21.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:03.309250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum21.45
5-th percentile21.57
Q121.58
median21.6
Q321.61
95-th percentile21.61
Maximum21.61
Range0.16
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.018116
Coefficient of variation (CV)0.00083886523
Kurtosis5.9194816
Mean21.595841
Median Absolute Deviation (MAD)0.01
Skewness-1.6809381
Sum533849.19
Variance0.00032818946
MonotonicityNot monotonic
2024-06-01T20:43:03.440027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
21.61 12186
49.3%
21.6 3306
 
13.4%
21.58 3098
 
12.5%
21.57 3082
 
12.5%
21.59 2075
 
8.4%
21.56 734
 
3.0%
21.55 57
 
0.2%
21.54 31
 
0.1%
21.53 26
 
0.1%
21.52 24
 
0.1%
Other values (7) 101
 
0.4%
ValueCountFrequency (%)
21.45 9
 
< 0.1%
21.46 12
 
< 0.1%
21.47 14
0.1%
21.48 14
0.1%
21.49 16
0.1%
21.5 16
0.1%
21.51 20
0.1%
21.52 24
0.1%
21.53 26
0.1%
21.54 31
0.1%
ValueCountFrequency (%)
21.61 12186
49.3%
21.6 3306
 
13.4%
21.59 2075
 
8.4%
21.58 3098
 
12.5%
21.57 3082
 
12.5%
21.56 734
 
3.0%
21.55 57
 
0.2%
21.54 31
 
0.1%
21.53 26
 
0.1%
21.52 24
 
0.1%

Sensor Measure 7
Real number (ℝ)

HIGH CORRELATION 

Distinct1854
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean555.14381
Minimum549.61
Maximum570.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:03.592539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum549.61
5-th percentile551.64
Q1553.11
median554.05
Q3556.04
95-th percentile563.16
Maximum570.49
Range20.88
Interquartile range (IQR)2.93

Descriptive statistics

Standard deviation3.4373429
Coefficient of variation (CV)0.0061918062
Kurtosis3.312221
Mean555.14381
Median Absolute Deviation (MAD)1.21
Skewness1.8040953
Sum13723155
Variance11.815326
MonotonicityNot monotonic
2024-06-01T20:43:03.753702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
553.91 88
 
0.4%
553.6 85
 
0.3%
553.65 82
 
0.3%
553.9 82
 
0.3%
553.58 80
 
0.3%
553.45 78
 
0.3%
553.72 78
 
0.3%
553.48 77
 
0.3%
553.83 77
 
0.3%
553.97 75
 
0.3%
Other values (1844) 23918
96.8%
ValueCountFrequency (%)
549.61 1
< 0.1%
549.66 1
< 0.1%
549.76 1
< 0.1%
549.77 2
< 0.1%
549.81 1
< 0.1%
549.83 2
< 0.1%
549.84 2
< 0.1%
549.85 1
< 0.1%
549.86 1
< 0.1%
549.87 1
< 0.1%
ValueCountFrequency (%)
570.49 1
< 0.1%
570.23 1
< 0.1%
570.22 1
< 0.1%
570 1
< 0.1%
569.77 1
< 0.1%
569.74 1
< 0.1%
569.73 1
< 0.1%
569.7 1
< 0.1%
569.69 1
< 0.1%
569.67 1
< 0.1%

Sensor Measure 8
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0716
Minimum2386.9
Maximum2388.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:03.932433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2386.9
5-th percentile2387.91
Q12388
median2388.07
Q32388.14
95-th percentile2388.32
Maximum2388.6
Range1.7
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.15828493
Coefficient of variation (CV)6.6281485 × 10-5
Kurtosis8.4313109
Mean2388.0716
Median Absolute Deviation (MAD)0.07
Skewness-1.3586264
Sum59033129
Variance0.025054119
MonotonicityNot monotonic
2024-06-01T20:43:04.091879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.04 1032
 
4.2%
2388.07 1014
 
4.1%
2388.05 1010
 
4.1%
2388.02 981
 
4.0%
2388.06 979
 
4.0%
2388.09 970
 
3.9%
2388.03 965
 
3.9%
2388.08 960
 
3.9%
2388.1 891
 
3.6%
2388.01 881
 
3.6%
Other values (151) 15037
60.8%
ValueCountFrequency (%)
2386.9 1
 
< 0.1%
2386.93 1
 
< 0.1%
2386.98 1
 
< 0.1%
2386.99 3
< 0.1%
2387 4
< 0.1%
2387.01 2
< 0.1%
2387.02 2
< 0.1%
2387.03 3
< 0.1%
2387.04 2
< 0.1%
2387.05 1
 
< 0.1%
ValueCountFrequency (%)
2388.6 3
 
< 0.1%
2388.59 7
 
< 0.1%
2388.58 5
 
< 0.1%
2388.57 4
 
< 0.1%
2388.56 18
0.1%
2388.55 16
0.1%
2388.54 20
0.1%
2388.53 31
0.1%
2388.52 34
0.1%
2388.51 35
0.1%

Sensor Measure 9
Real number (ℝ)

HIGH CORRELATION 

Distinct7114
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9064.1108
Minimum9017.98
Maximum9234.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:04.237019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9017.98
5-th percentile9041.27
Q19051.92
median9060.01
Q39070.0925
95-th percentile9105.8605
Maximum9234.35
Range216.37
Interquartile range (IQR)18.1725

Descriptive statistics

Standard deviation19.980294
Coefficient of variation (CV)0.0022043303
Kurtosis6.2906672
Mean9064.1108
Median Absolute Deviation (MAD)8.91
Skewness1.9049369
Sum2.2406482 × 108
Variance399.21216
MonotonicityNot monotonic
2024-06-01T20:43:04.430434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9058.22 18
 
0.1%
9050.22 18
 
0.1%
9063.88 16
 
0.1%
9055.15 16
 
0.1%
9058.67 16
 
0.1%
9051.89 15
 
0.1%
9061.31 15
 
0.1%
9057.2 15
 
0.1%
9057.55 15
 
0.1%
9058.04 15
 
0.1%
Other values (7104) 24561
99.4%
ValueCountFrequency (%)
9017.98 1
< 0.1%
9018.74 1
< 0.1%
9020.61 1
< 0.1%
9021.05 1
< 0.1%
9021.93 1
< 0.1%
9023.7 1
< 0.1%
9023.94 1
< 0.1%
9024.2 1
< 0.1%
9024.36 1
< 0.1%
9024.45 1
< 0.1%
ValueCountFrequency (%)
9234.35 1
< 0.1%
9228.91 1
< 0.1%
9228.33 1
< 0.1%
9221.2 1
< 0.1%
9220.14 1
< 0.1%
9217.75 1
< 0.1%
9216.38 1
< 0.1%
9212.03 1
< 0.1%
9211.16 1
< 0.1%
9209.17 1
< 0.1%

Sensor Measure 10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1.3
21814 
1.31
2740 
1.32
 
157
1.29
 
9

Length

Max length4
Median length3
Mean length3.1175566
Min length3

Characters and Unicode

Total characters77066
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.3
2nd row1.3
3rd row1.3
4th row1.3
5th row1.3

Common Values

ValueCountFrequency (%)
1.3 21814
88.2%
1.31 2740
 
11.1%
1.32 157
 
0.6%
1.29 9
 
< 0.1%

Length

2024-06-01T20:43:04.636106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:43:04.764445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.3 21814
88.2%
1.31 2740
 
11.1%
1.32 157
 
0.6%
1.29 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 27460
35.6%
. 24720
32.1%
3 24711
32.1%
2 166
 
0.2%
9 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 27460
35.6%
. 24720
32.1%
3 24711
32.1%
2 166
 
0.2%
9 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 27460
35.6%
. 24720
32.1%
3 24711
32.1%
2 166
 
0.2%
9 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 27460
35.6%
. 24720
32.1%
3 24711
32.1%
2 166
 
0.2%
9 9
 
< 0.1%

Sensor Measure 11
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.415707
Minimum46.69
Maximum48.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:04.894424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum46.69
5-th percentile47.01
Q147.19
median47.36
Q347.6
95-th percentile48
Maximum48.44
Range1.75
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation0.30007416
Coefficient of variation (CV)0.0063285814
Kurtosis-0.13481323
Mean47.415707
Median Absolute Deviation (MAD)0.2
Skewness0.63348758
Sum1172116.3
Variance0.090044502
MonotonicityNot monotonic
2024-06-01T20:43:05.135607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.19 398
 
1.6%
47.26 391
 
1.6%
47.22 386
 
1.6%
47.17 379
 
1.5%
47.24 375
 
1.5%
47.2 370
 
1.5%
47.25 368
 
1.5%
47.28 365
 
1.5%
47.3 363
 
1.5%
47.29 361
 
1.5%
Other values (160) 20964
84.8%
ValueCountFrequency (%)
46.69 1
 
< 0.1%
46.71 2
 
< 0.1%
46.73 1
 
< 0.1%
46.75 1
 
< 0.1%
46.77 2
 
< 0.1%
46.78 6
< 0.1%
46.79 2
 
< 0.1%
46.8 7
< 0.1%
46.81 6
< 0.1%
46.82 6
< 0.1%
ValueCountFrequency (%)
48.44 2
 
< 0.1%
48.43 2
 
< 0.1%
48.41 1
 
< 0.1%
48.4 1
 
< 0.1%
48.39 1
 
< 0.1%
48.38 2
 
< 0.1%
48.37 3
< 0.1%
48.35 4
< 0.1%
48.34 2
 
< 0.1%
48.33 6
< 0.1%

Sensor Measure 12
Real number (ℝ)

HIGH CORRELATION 

Distinct1772
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean523.05087
Minimum517.77
Maximum537.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:05.305547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum517.77
5-th percentile519.9
Q1521.15
median521.98
Q3523.84
95-th percentile530.71
Maximum537.4
Range19.63
Interquartile range (IQR)2.69

Descriptive statistics

Standard deviation3.255314
Coefficient of variation (CV)0.0062237043
Kurtosis3.5130826
Mean523.05087
Median Absolute Deviation (MAD)1.08
Skewness1.8662278
Sum12929818
Variance10.597069
MonotonicityNot monotonic
2024-06-01T20:43:05.494751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.69 93
 
0.4%
521.8 92
 
0.4%
521.2 92
 
0.4%
521.99 89
 
0.4%
521.84 88
 
0.4%
522.02 87
 
0.4%
521.54 87
 
0.4%
521.47 86
 
0.3%
521.58 86
 
0.3%
521.74 85
 
0.3%
Other values (1762) 23835
96.4%
ValueCountFrequency (%)
517.77 1
< 0.1%
517.78 1
< 0.1%
517.97 1
< 0.1%
518.01 2
< 0.1%
518.03 1
< 0.1%
518.05 1
< 0.1%
518.08 1
< 0.1%
518.11 1
< 0.1%
518.12 1
< 0.1%
518.14 1
< 0.1%
ValueCountFrequency (%)
537.4 1
 
< 0.1%
537.35 1
 
< 0.1%
537.11 1
 
< 0.1%
536.82 3
< 0.1%
536.81 1
 
< 0.1%
536.75 1
 
< 0.1%
536.74 1
 
< 0.1%
536.72 1
 
< 0.1%
536.71 1
 
< 0.1%
536.7 1
 
< 0.1%

Sensor Measure 13
Real number (ℝ)

HIGH CORRELATION 

Distinct163
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0716
Minimum2386.93
Maximum2388.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:05.670784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2386.93
5-th percentile2387.91
Q12388.01
median2388.07
Q32388.14
95-th percentile2388.32
Maximum2388.61
Range1.68
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.15812067
Coefficient of variation (CV)6.6212699 × 10-5
Kurtosis8.3813811
Mean2388.0716
Median Absolute Deviation (MAD)0.07
Skewness-1.3524134
Sum59033131
Variance0.025002146
MonotonicityNot monotonic
2024-06-01T20:43:05.831487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.05 1080
 
4.4%
2388.04 1019
 
4.1%
2388.06 1016
 
4.1%
2388.07 1014
 
4.1%
2388.03 997
 
4.0%
2388.02 994
 
4.0%
2388.1 957
 
3.9%
2388.08 945
 
3.8%
2388.09 943
 
3.8%
2388.11 853
 
3.5%
Other values (153) 14902
60.3%
ValueCountFrequency (%)
2386.93 1
 
< 0.1%
2386.94 1
 
< 0.1%
2387 3
< 0.1%
2387.01 1
 
< 0.1%
2387.02 4
< 0.1%
2387.03 2
< 0.1%
2387.04 3
< 0.1%
2387.05 2
< 0.1%
2387.06 1
 
< 0.1%
2387.07 1
 
< 0.1%
ValueCountFrequency (%)
2388.61 1
 
< 0.1%
2388.6 2
 
< 0.1%
2388.59 4
 
< 0.1%
2388.58 2
 
< 0.1%
2388.57 7
 
< 0.1%
2388.56 12
 
< 0.1%
2388.55 17
 
0.1%
2388.54 13
 
0.1%
2388.53 45
0.2%
2388.52 37
0.1%

Sensor Measure 14
Real number (ℝ)

HIGH CORRELATION 

Distinct6320
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8144.2029
Minimum8099.68
Maximum8290.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:06.013474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8099.68
5-th percentile8123.8795
Q18134.51
median8141.2
Q38149.23
95-th percentile8177.97
Maximum8290.55
Range190.87
Interquartile range (IQR)14.72

Descriptive statistics

Standard deviation16.504118
Coefficient of variation (CV)0.0020264866
Kurtosis6.6924814
Mean8144.2029
Median Absolute Deviation (MAD)7.235
Skewness1.8623365
Sum2.013247 × 108
Variance272.3859
MonotonicityNot monotonic
2024-06-01T20:43:06.198969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8142.44 20
 
0.1%
8140.01 20
 
0.1%
8138.89 19
 
0.1%
8143.12 19
 
0.1%
8139.98 18
 
0.1%
8139.81 18
 
0.1%
8141.06 18
 
0.1%
8139.54 17
 
0.1%
8143.61 17
 
0.1%
8141.4 17
 
0.1%
Other values (6310) 24537
99.3%
ValueCountFrequency (%)
8099.68 1
< 0.1%
8099.9 1
< 0.1%
8100.19 1
< 0.1%
8100.35 1
< 0.1%
8100.6 1
< 0.1%
8101.28 1
< 0.1%
8102.21 1
< 0.1%
8102.92 1
< 0.1%
8103.12 1
< 0.1%
8103.29 1
< 0.1%
ValueCountFrequency (%)
8290.55 1
< 0.1%
8278.39 1
< 0.1%
8277.76 1
< 0.1%
8276.38 1
< 0.1%
8273.46 1
< 0.1%
8272.95 1
< 0.1%
8272.42 1
< 0.1%
8269.89 1
< 0.1%
8268.86 1
< 0.1%
8268.62 1
< 0.1%

Sensor Measure 15
Real number (ℝ)

HIGH CORRELATION 

Distinct3122
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3961758
Minimum8.1563
Maximum8.5705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:06.367745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8.1563
5-th percentile8.284895
Q18.3606
median8.3983
Q38.437
95-th percentile8.492505
Maximum8.5705
Range0.4142
Interquartile range (IQR)0.0764

Descriptive statistics

Standard deviation0.060511614
Coefficient of variation (CV)0.0072070446
Kurtosis0.17546296
Mean8.3961758
Median Absolute Deviation (MAD)0.0382
Skewness-0.31446766
Sum207553.47
Variance0.0036616554
MonotonicityNot monotonic
2024-06-01T20:43:06.519623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3774 31
 
0.1%
8.3976 30
 
0.1%
8.3865 29
 
0.1%
8.3853 29
 
0.1%
8.3856 27
 
0.1%
8.4382 27
 
0.1%
8.4334 27
 
0.1%
8.3809 26
 
0.1%
8.4139 26
 
0.1%
8.3676 26
 
0.1%
Other values (3112) 24442
98.9%
ValueCountFrequency (%)
8.1563 1
< 0.1%
8.1889 1
< 0.1%
8.1932 1
< 0.1%
8.1951 1
< 0.1%
8.1961 1
< 0.1%
8.1965 1
< 0.1%
8.1979 1
< 0.1%
8.1984 1
< 0.1%
8.1986 1
< 0.1%
8.1992 1
< 0.1%
ValueCountFrequency (%)
8.5705 1
< 0.1%
8.5699 1
< 0.1%
8.5684 1
< 0.1%
8.5676 1
< 0.1%
8.5669 1
< 0.1%
8.5663 1
< 0.1%
8.5641 1
< 0.1%
8.5638 1
< 0.1%
8.5637 1
< 0.1%
8.5635 1
< 0.1%

Sensor Measure 16
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.03
24720 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters98880
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.03
2nd row0.03
3rd row0.03
4th row0.03
5th row0.03

Common Values

ValueCountFrequency (%)
0.03 24720
100.0%

Length

2024-06-01T20:43:06.672316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:43:06.786827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03 24720
100.0%

Most occurring characters

ValueCountFrequency (%)
0 49440
50.0%
. 24720
25.0%
3 24720
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49440
50.0%
. 24720
25.0%
3 24720
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49440
50.0%
. 24720
25.0%
3 24720
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49440
50.0%
. 24720
25.0%
3 24720
25.0%

Sensor Measure 17
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean392.56655
Minimum388
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:06.875719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum388
5-th percentile390
Q1391
median392
Q3394
95-th percentile396
Maximum399
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7614585
Coefficient of variation (CV)0.0044870316
Kurtosis-0.074069216
Mean392.56655
Median Absolute Deviation (MAD)1
Skewness0.39849615
Sum9704245
Variance3.1027361
MonotonicityNot monotonic
2024-06-01T20:43:07.058613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
392 5854
23.7%
393 4919
19.9%
391 4491
18.2%
394 3306
13.4%
390 2086
 
8.4%
395 1938
 
7.8%
396 1088
 
4.4%
389 495
 
2.0%
397 413
 
1.7%
398 79
 
0.3%
Other values (2) 51
 
0.2%
ValueCountFrequency (%)
388 40
 
0.2%
389 495
 
2.0%
390 2086
 
8.4%
391 4491
18.2%
392 5854
23.7%
393 4919
19.9%
394 3306
13.4%
395 1938
 
7.8%
396 1088
 
4.4%
397 413
 
1.7%
ValueCountFrequency (%)
399 11
 
< 0.1%
398 79
 
0.3%
397 413
 
1.7%
396 1088
 
4.4%
395 1938
 
7.8%
394 3306
13.4%
393 4919
19.9%
392 5854
23.7%
391 4491
18.2%
390 2086
 
8.4%

Sensor Measure 18
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2388
24720 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters98880
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2388
2nd row2388
3rd row2388
4th row2388
5th row2388

Common Values

ValueCountFrequency (%)
2388 24720
100.0%

Length

2024-06-01T20:43:07.190250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:43:07.295774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2388 24720
100.0%

Most occurring characters

ValueCountFrequency (%)
8 49440
50.0%
2 24720
25.0%
3 24720
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 49440
50.0%
2 24720
25.0%
3 24720
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 49440
50.0%
2 24720
25.0%
3 24720
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 49440
50.0%
2 24720
25.0%
3 24720
25.0%

Sensor Measure 19
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
100.0
24720 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters123600
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 24720
100.0%

Length

2024-06-01T20:43:07.399720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:43:07.496045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 24720
100.0%

Most occurring characters

ValueCountFrequency (%)
0 74160
60.0%
1 24720
 
20.0%
. 24720
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74160
60.0%
1 24720
 
20.0%
. 24720
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74160
60.0%
1 24720
 
20.0%
. 24720
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74160
60.0%
1 24720
 
20.0%
. 24720
 
20.0%

Sensor Measure 20
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.988552
Minimum38.17
Maximum39.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:07.617800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum38.17
5-th percentile38.57
Q138.83
median38.99
Q339.14
95-th percentile39.42
Maximum39.85
Range1.68
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.24886469
Coefficient of variation (CV)0.0063830197
Kurtosis0.16073022
Mean38.988552
Median Absolute Deviation (MAD)0.16
Skewness0.11532171
Sum963797
Variance0.061933636
MonotonicityNot monotonic
2024-06-01T20:43:07.779264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.02 458
 
1.9%
39.01 449
 
1.8%
39.05 446
 
1.8%
39.03 436
 
1.8%
38.96 435
 
1.8%
39.04 434
 
1.8%
38.92 432
 
1.7%
39 428
 
1.7%
39.07 422
 
1.7%
38.97 422
 
1.7%
Other values (155) 20358
82.4%
ValueCountFrequency (%)
38.17 1
 
< 0.1%
38.2 5
< 0.1%
38.22 1
 
< 0.1%
38.23 3
< 0.1%
38.24 3
< 0.1%
38.25 4
< 0.1%
38.26 2
 
< 0.1%
38.27 3
< 0.1%
38.28 7
< 0.1%
38.29 3
< 0.1%
ValueCountFrequency (%)
39.85 1
 
< 0.1%
39.84 1
 
< 0.1%
39.82 2
 
< 0.1%
39.81 2
 
< 0.1%
39.8 2
 
< 0.1%
39.79 4
< 0.1%
39.78 5
< 0.1%
39.77 2
 
< 0.1%
39.76 8
< 0.1%
39.75 8
< 0.1%

Sensor Measure 21
Real number (ℝ)

HIGH CORRELATION 

Distinct6440
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.393024
Minimum22.8726
Maximum23.9505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:07.964177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum22.8726
5-th percentile23.147695
Q123.2962
median23.3916
Q323.4833
95-th percentile23.656105
Maximum23.9505
Range1.0779
Interquartile range (IQR)0.1871

Descriptive statistics

Standard deviation0.14923379
Coefficient of variation (CV)0.0063794141
Kurtosis0.18039451
Mean23.393024
Median Absolute Deviation (MAD)0.0938
Skewness0.16426911
Sum578275.55
Variance0.022270723
MonotonicityNot monotonic
2024-06-01T20:43:08.177727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3923 18
 
0.1%
23.3972 17
 
0.1%
23.377 17
 
0.1%
23.3995 16
 
0.1%
23.448 16
 
0.1%
23.4429 16
 
0.1%
23.4567 16
 
0.1%
23.4678 15
 
0.1%
23.4119 15
 
0.1%
23.3562 15
 
0.1%
Other values (6430) 24559
99.3%
ValueCountFrequency (%)
22.8726 1
< 0.1%
22.8995 1
< 0.1%
22.9311 1
< 0.1%
22.9464 1
< 0.1%
22.9494 1
< 0.1%
22.95 1
< 0.1%
22.9502 1
< 0.1%
22.9537 1
< 0.1%
22.9538 1
< 0.1%
22.954 1
< 0.1%
ValueCountFrequency (%)
23.9505 1
< 0.1%
23.9159 1
< 0.1%
23.9154 1
< 0.1%
23.9075 1
< 0.1%
23.9022 1
< 0.1%
23.8974 1
< 0.1%
23.8971 1
< 0.1%
23.894 1
< 0.1%
23.8881 2
< 0.1%
23.8879 1
< 0.1%

RUL
Real number (ℝ)

HIGH CORRELATION 

Distinct525
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.07706
Minimum1
Maximum525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size193.2 KiB
2024-06-01T20:43:09.899506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q162
median124
Q3191
95-th percentile333.05
Maximum525
Range524
Interquartile range (IQR)129

Descriptive statistics

Standard deviation98.846675
Coefficient of variation (CV)0.71073312
Kurtosis0.82037479
Mean139.07706
Median Absolute Deviation (MAD)64
Skewness0.96779702
Sum3437985
Variance9770.6652
MonotonicityNot monotonic
2024-06-01T20:43:10.060520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 100
 
0.4%
130 100
 
0.4%
96 100
 
0.4%
95 100
 
0.4%
94 100
 
0.4%
93 100
 
0.4%
92 100
 
0.4%
91 100
 
0.4%
90 100
 
0.4%
89 100
 
0.4%
Other values (515) 23720
96.0%
ValueCountFrequency (%)
1 100
0.4%
2 100
0.4%
3 100
0.4%
4 100
0.4%
5 100
0.4%
6 100
0.4%
7 100
0.4%
8 100
0.4%
9 100
0.4%
10 100
0.4%
ValueCountFrequency (%)
525 1
< 0.1%
524 1
< 0.1%
523 1
< 0.1%
522 1
< 0.1%
521 1
< 0.1%
520 1
< 0.1%
519 1
< 0.1%
518 1
< 0.1%
517 1
< 0.1%
516 1
< 0.1%

Interactions

2024-06-01T20:42:56.015707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:01.676944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:04.285242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:06.947968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:09.717526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:12.344548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:14.852740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:17.503387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:20.309232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:24.574939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:27.215497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:29.865113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:32.786648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:35.739360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:38.733092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:41.760876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:44.658046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:47.323368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:50.169872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:53.066855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:56.111630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:01.803312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:04.417241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:07.083171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:09.829952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:12.480680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:14.972733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:17.631680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:20.482999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:24.689631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:27.360041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:30.001343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:32.939688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:35.883811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:38.851444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:41.925828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:44.787540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:47.476328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:50.310897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:53.189328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:56.214777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:01.916040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:04.538155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:07.234745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:09.966783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:12.594324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:15.087681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:17.760870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:20.621901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:24.809610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:27.519633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:30.113533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:33.053517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:36.006578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:38.991188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:42.055624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:44.901504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:47.595595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:50.455208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:53.342813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:56.312248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:02.049332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:04.661388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:07.386369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:10.119141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:12.730866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:15.208674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:17.903009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:20.791880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:24.945848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-06-01T20:42:26.312494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:28.975445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:31.774750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:34.691653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:37.579554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:40.691815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:43.594972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:46.304158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:49.196904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:52.054851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:55.188837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:57.981952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:03.519434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:06.153396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:08.939959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:11.559345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:14.098497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:16.584413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:19.521210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:22.424839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:26.426857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:29.128253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:31.956457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:34.876754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:37.780976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:40.844057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:43.737731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:46.441961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:49.327584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:52.210967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:55.322459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:58.114139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:03.657362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:06.299003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:09.113929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:11.695396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:14.243444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:16.732068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:19.658091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:22.586551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:26.555748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:29.248773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:32.112829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:35.045157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:37.968951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:41.042467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:43.894631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:46.570227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:49.475707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:52.355944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:55.484117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:58.211793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:03.769787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:06.400889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:09.226876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:11.823749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:14.363897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:16.895233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:19.788451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:22.691013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:26.689079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:29.356704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:32.243685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:35.181745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:38.098009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:41.171774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:44.056429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:46.722794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:49.608869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:52.519894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:55.564981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:58.324606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:03.891381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:06.537400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:09.355269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:11.944184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:14.484776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:17.074064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:19.900095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:24.129783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:26.835572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:29.493580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:32.370243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:35.334320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:38.243835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:41.308681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:44.228751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:46.875405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:49.744577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:52.646928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:55.661320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:58.519504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:04.019536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:06.685789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:09.486312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:12.086496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:14.597746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:17.217987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:20.034730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:24.299064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:26.994253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:29.627855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:32.498655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:35.493357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:38.419647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:41.468722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:44.365719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:47.053083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:49.889832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:52.779821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:55.793048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:58.647400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:04.140460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:06.830975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:09.603962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:12.207159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:14.722709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:17.338836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:20.187977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:24.453336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:27.107423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:29.741485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:32.635660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:35.617393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:38.572778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:41.632858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:44.513383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:47.181957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:50.006338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:52.929295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:42:55.908335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-06-01T20:43:10.196713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CycleOperation Setting 1Operation Setting 2RULSensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 17Sensor Measure 2Sensor Measure 20Sensor Measure 21Sensor Measure 3Sensor Measure 4Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9UnitNumber
Cycle1.000-0.0060.003-0.5790.3390.3920.2730.4480.464-0.2930.3820.3110.2210.2260.3680.360-0.3010.2730.4480.485-0.075
Operation Setting 1-0.0061.0000.0000.0020.000-0.007-0.0060.000-0.0090.0050.001-0.003-0.007-0.001-0.004-0.002-0.001-0.0020.000-0.008-0.004
Operation Setting 20.0030.0001.000-0.0020.0130.000-0.004-0.0080.0070.0050.0020.005-0.003-0.0050.0040.0030.001-0.008-0.0040.002-0.015
RUL-0.5790.002-0.0021.0000.284-0.758-0.091-0.740-0.490-0.090-0.700-0.6480.0870.088-0.673-0.725-0.253-0.079-0.739-0.589-0.075
Sensor Measure 100.3390.0000.0130.2841.0000.3810.5560.5190.510-0.5380.3580.2710.5260.5280.3460.332-0.2230.5560.5190.5070.031
Sensor Measure 110.392-0.0070.000-0.7580.3811.000-0.0150.8170.3560.2460.7590.737-0.211-0.2170.7180.8250.435-0.0300.8180.4890.031
Sensor Measure 120.273-0.006-0.004-0.0910.556-0.0151.0000.2490.576-0.7970.027-0.0900.8090.8100.031-0.072-0.2620.9500.2470.4940.108
Sensor Measure 130.4480.000-0.008-0.7400.5190.8170.2491.0000.4240.0030.7370.6780.0360.0330.7020.7640.3450.2350.9050.5380.046
Sensor Measure 140.464-0.0090.007-0.4900.5100.3560.5760.4241.000-0.3730.3660.2670.3900.3920.3580.3140.0410.5630.4260.8920.079
Sensor Measure 15-0.2930.0050.005-0.090-0.5380.246-0.7970.003-0.3731.0000.1830.280-0.824-0.8270.1670.2890.597-0.8040.004-0.261-0.042
Sensor Measure 170.3820.0010.002-0.7000.3580.7590.0270.7370.3660.1831.0000.651-0.150-0.1570.6530.7230.3770.0130.7380.4810.026
Sensor Measure 20.311-0.0030.005-0.6480.2710.737-0.0900.6780.2670.2800.6511.000-0.252-0.2550.6200.7140.405-0.1030.6800.3880.021
Sensor Measure 200.221-0.007-0.0030.0870.526-0.2110.8090.0360.390-0.824-0.150-0.2521.0000.811-0.137-0.257-0.4700.8150.0330.2870.056
Sensor Measure 210.226-0.001-0.0050.0880.528-0.2170.8100.0330.392-0.827-0.157-0.2550.8111.000-0.145-0.260-0.4740.8130.0300.2870.054
Sensor Measure 30.368-0.0040.004-0.6730.3460.7180.0310.7020.3580.1670.6530.620-0.137-0.1451.0000.6890.3580.0170.7050.4610.024
Sensor Measure 40.360-0.0020.003-0.7250.3320.825-0.0720.7640.3140.2890.7230.714-0.257-0.2600.6891.0000.435-0.0870.7640.4460.029
Sensor Measure 6-0.301-0.0010.001-0.253-0.2230.435-0.2620.3450.0410.5970.3770.405-0.470-0.4740.3580.4351.000-0.2820.3460.1440.023
Sensor Measure 70.273-0.002-0.008-0.0790.556-0.0300.9500.2350.563-0.8040.013-0.1030.8150.8130.017-0.087-0.2821.0000.2330.4790.104
Sensor Measure 80.4480.000-0.004-0.7390.5190.8180.2470.9050.4260.0040.7380.6800.0330.0300.7050.7640.3460.2331.0000.5390.047
Sensor Measure 90.485-0.0080.002-0.5890.5070.4890.4940.5380.892-0.2610.4810.3880.2870.2870.4610.4460.1440.4790.5391.0000.077
UnitNumber-0.075-0.004-0.015-0.0750.0310.0310.1080.0460.079-0.0420.0260.0210.0560.0540.0240.0290.0230.1040.0470.0771.000

Missing values

2024-06-01T20:42:58.922691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-01T20:42:59.402708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UnitNumberCycleOperation Setting 1Operation Setting 2Operation Setting 3Sensor Measure 1Sensor Measure 2Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 20Sensor Measure 21RUL
011-0.00050.0004100.0518.67642.361583.231396.8414.6221.61553.972387.969062.171.347.30522.312388.018145.328.42460.033912388100.039.1123.3537259
1120.0008-0.0003100.0518.67642.501584.691396.8914.6221.61554.552388.009061.781.347.23522.422388.038152.858.44030.033922388100.038.9923.4491258
213-0.0014-0.0002100.0518.67642.181582.351405.6114.6221.61554.432388.039070.231.347.22522.032388.008150.178.39010.033912388100.038.8523.3669257
314-0.00200.0001100.0518.67642.921585.611392.2714.6221.61555.212388.009064.571.347.24522.492388.088146.568.38780.033922388100.038.9623.2951256
4150.00160.0000100.0518.67641.681588.631397.6514.6221.61554.742388.049076.141.347.15522.582388.038147.808.38690.033922388100.039.1423.4583255
5160.0011-0.0005100.0518.67642.241584.091400.0114.6221.61554.752388.009074.981.347.07522.422388.028144.928.41520.033932388100.038.9223.4281254
617-0.00380.0002100.0518.67642.581585.611401.0914.6221.61554.582388.049074.651.347.38522.462388.028147.058.38420.033912388100.038.8423.4087253
718-0.0007-0.0005100.0518.67642.321588.321397.0814.6221.60554.852388.069063.541.347.31522.102387.978157.348.41900.033912388100.039.0523.4590252
819-0.00260.0000100.0518.67641.641587.811406.5114.6221.61554.132388.049067.201.347.31521.832388.018147.208.40910.033922388100.038.9923.4693251
91100.0019-0.0002100.0518.67642.421587.391402.2514.6221.61554.782387.999070.041.347.35521.962387.998152.868.39870.033912388100.038.9423.4781250
UnitNumberCycleOperation Setting 1Operation Setting 2Operation Setting 3Sensor Measure 1Sensor Measure 2Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 20Sensor Measure 21RUL
24710100143-0.00070.0000100.0518.67643.921602.911420.5114.6221.61551.542388.259059.281.348.12519.822388.208136.628.52560.033972388100.038.6123.141210
247111001440.0020-0.0003100.0518.67643.831599.111428.2114.6221.61551.332388.249062.801.348.29519.762388.218137.628.50690.033942388100.038.4323.13919
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